用大模型从患者自述文本中自动提取关键经验信息，助力精准医疗研究。
PVminerLLM: Structured Extraction of Patient Voice from Patient-Generated Text using Large Language Models
- 基于监督微调的大模型专门处理患者自述文本的结构化提取。
- 在多个数据集上最高达83.82%的代码预测F1值，小模型也表现优异。
- 适合临床研究、健康公平分析等需要挖掘患者真实体验的场景。
患者生成的文本蕴含其生活经历、社会背景及医疗参与情况，这些非临床因素深刻影响治疗依从性、照护协调与健康公平。但此类信息多为非结构化，难以用于以患者为中心的研究和质量改进。为此，我们提出PVminer基准和PVminerLLM——一个针对该任务定制的监督微调大模型。在多个数据集与模型规模下，其显著优于提示工程基线，代码预测最高达83.82% F1，子代码预测80.74% F1，证据片段提取87.03% F1。尤其值得注意的是，小模型亦能取得良好性能，表明无需超大规模模型即可实现可靠提取。研究成果支持对患者自述文本中社会与体验信号的可扩展分析。代码、评估脚本及训练好的模型将公开发布，标注数据集将按需提供用于研究。
原文摘要 · Abstract (English)
Motivation: Patient-generated text contains critical information about patients' lived experiences, social circumstances, and engagement in care, including factors that strongly influence adherence, care coordination, and health equity. However, these patient voice signals are rarely available in structured form, limiting their use in patient-centered outcomes research and clinical quality improvement. Reliable extraction of such information is therefore essential for understanding and addressing non-clinical drivers of health outcomes at scale. Results: We introduce PVminer, a benchmark for structured extraction of patient voice, and propose PVminerLLM, a supervised fine-tuned large language model tailored to this task. Across multiple datasets and model sizes, PVminerLLM substantially outperforms prompt-based baselines, achieving up to 83.82% F1 for Code prediction, 80.74% F1 for Sub-code prediction, and 87.03% F1 for evidence Span extraction. Notably, strong performance is achieved even with smaller models, demonstrating that reliable patient voice extraction is feasible without extreme model scale. These results enable scalable analysis of social and experiential signals embedded in patient-generated text. Availability and Implementation: Code, evaluation scripts, and trained LLMs will be released publicly. Annotated datasets will be made available upon request for research use. Keywords: Large Language Models, Supervised Fine-Tuning, Medical Annotation, Patient-Generated Text, Clinical NLP
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